Inductive Logic Programming

Inductive Logic Programming (ILP) is a machine learning approach that aims to automatically derive interpretable rules from data using logical representation. The goal of ILP is to combine logical reasoning and statistical learning to discover structured patterns in the data and generate hypotheses that can generalize to new instances. Its core advantage lies in the ability to handle complex relational data, providing transparent and easily understandable models, which are widely applied in knowledge discovery, data mining, and expert systems.